Dangerous Degree Evaluation of Mine Debris Flow Based on the Immune Genetic Neural Network
Автор: Xicheng Xue, Jisong Bi, Lingling Chen
Журнал: International Journal of Education and Management Engineering(IJEME) @ijeme
Статья в выпуске: 3 vol.2, 2012 года.
Бесплатный доступ
Taking the western Qinling Mountain, in the southern Shaanxi Province of china, as an example, based upon comprehensive analysis of geological data for 20 debris flow gullies, the author has put forward a series of indices system and has developed the immune genetic neural network system, which can quantitatively evaluate the dangerous degree of mine debris flow. This software system manage initial data through Access’s data-base technology, and determine and optimize the hidden layer network by immune genetic algorithm, as well as achieve the dangerous degree evaluation of mine debris flow by virtue of artificial neural network which has been successfully trained. The calculating results of mine debris flow examples testify that this method is reliable and can accurately evaluate the dangerous degree of mine debris flow. These evaluation results have some important instructive significance for the disaster prevention and reduction of mine debris flow.
Qinling Mountain, Mine Debris Flow, Dangerous Degree, Indices System, Immune Genetic Algorithm, Artificial Neural Network
Короткий адрес: https://sciup.org/15013676
IDR: 15013676
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